format with isort and black

benchmarks/hypernetx.ipynb

@@ -15,10 +15,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import time\n",
+    "\n",
     "import hypernetx as hnx\n",
-    "import xgi\n",
     "import pandas as pd\n",
-    "import time"
+    "\n",
+    "import xgi"
    ]
   },
   {

benchmarks/networkx.ipynb

@@ -15,9 +15,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import time\n",
+    "\n",
     "import networkx as nx\n",
-    "import xgi\n",
-    "import time"
+    "\n",
+    "import xgi"
    ]
   },
   {

docs/source/api/recipes/recipes.ipynb

@@ -15,6 +15,7 @@
    "outputs": [],
    "source": [
     "import networkx as nx\n",
+    "\n",
     "import xgi"
    ]
   },
@@ -52,10 +53,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xgi\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from numpy.linalg import eigh"
+    "from numpy.linalg import eigh\n",
+    "\n",
+    "import xgi"
    ]
   },
   {
@@ -112,9 +114,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xgi\n",
     "from itertools import permutations\n",
     "\n",
+    "import xgi\n",
+    "\n",
     "\n",
     "def adjacency_tensor(H, order):\n",
     "    N = H.num_nodes\n",
@@ -411,10 +414,11 @@
     }
    ],
    "source": [
-    "import xgi\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
     "\n",
+    "import xgi\n",
+    "\n",
     "H = xgi.load_xgi_data(\"diseasome\")\n",
     "H.merge_duplicate_edges(rename=\"tuple\", multiplicity=\"weight\")\n",
     "\n",
@@ -456,11 +460,11 @@
     }
    ],
    "source": [
-    "import xgi\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
     "\n",
+    "import xgi\n",
     "\n",
     "H = xgi.load_xgi_data(\"diseasome\")\n",
     "df1 = H.nodes.degree.ashist(bin_edges=True)\n",
@@ -554,9 +558,10 @@
     }
    ],
    "source": [
-    "import xgi\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "import xgi\n",
+    "\n",
     "H = xgi.random_hypergraph(N=10, ps=[0.2, 0.05, 0.05], seed=1)\n",
     "\n",
     "_, ax = plt.subplots(figsize=(4, 4), subplot_kw={\"projection\": \"3d\"})\n",
@@ -731,7 +736,7 @@
     "H = xgi.load_xgi_data(\"email-enron\")\n",
     "print(H.num_nodes)\n",
     "\n",
-    "# Get all of the dates \n",
+    "# Get all of the dates\n",
     "nodes = H.nodes.filterby(\"degree\", [3, 20], mode=outsiderange)\n",
     "print(len(nodes))"
    ]
@@ -758,16 +763,20 @@
     }
    ],
    "source": [
-    "import xgi\n",
     "import datetime\n",
+    "\n",
+    "import xgi\n",
+    "\n",
     "date1 = datetime.datetime(2000, 1, 1)\n",
     "date2 = datetime.datetime(2001, 1, 1)\n",
-    "datecompare = lambda date, arg: arg[0] <= datetime.datetime.fromisoformat(date) <= arg[1]\n",
+    "datecompare = (\n",
+    "    lambda date, arg: arg[0] <= datetime.datetime.fromisoformat(date) <= arg[1]\n",
+    ")\n",
     "\n",
     "H = xgi.load_xgi_data(\"email-enron\")\n",
     "print(H.num_edges)\n",
     "\n",
-    "# Get all of the dates \n",
+    "# Get all of the dates\n",
     "e = H.edges.filterby_attr(\"timestamp\", [date1, date2], mode=datecompare)\n",
     "print(len(e))"
    ]

xgi/core/diviews.py

@@ -201,7 +201,7 @@ def filterby(self, stat, val, mode="eq"):
             * 'geq': Return IDs whose value is greater than or equal to `val`.
             * 'between': In this mode, `val` must be a tuple `(val1, val2)`.  Return IDs
               whose value `v` satisfies `val1 <= v <= val2`.
-            * function, must be able to call `mode(attribute, val)` and have it map to a bool.
+            * function, must be able to call `mode(statistic, val)` and have it map to a bool.
 
         See Also
         --------

xgi/core/views.py

@@ -194,7 +194,7 @@ def filterby(self, stat, val, mode="eq"):
             * 'geq': Return IDs whose value is greater than or equal to `val`.
             * 'between': In this mode, `val` must be a tuple `(val1, val2)`.  Return IDs
               whose value `v` satisfies `val1 <= v <= val2`.
-            * function, must be able to call `mode(attribute, val)` and have it map to a bool.
+            * function, must be able to call `mode(statistic, val)` and have it map to a bool.
 
         See Also
         --------